Closing nature connectedness to foster environmental culture: investigating urban residents’ utilization and contribution to parks in Vietnam
Bibliographic record
Abstract
Abstract This study aims to investigate the potential for enhancing financial contributions to nature conservation and/or environment protection contributed by citizens and/or the private sector in Vietnam. Specifically, the study empirically examines the antecedents of the utilization patterns and financial contribution of city dwellers to public and national parks. We introduce and use a novel CBMM analytic method (Bayesian mindsponge mindspongeconomics combined with Culture Tower) to analyze the dataset of 535 Vietnamese urban residents surveyed in late 2020. Empirical results demonstrate that the more frequently individuals visit parks, the more likely they are to contribute financially to projects in parks as a whole. In addition, having trees in the house positively correlates with donations to national park conservation projects, while the intention to visit a national park in the next 12 months encourages urban residents to contribute to planting projects in public parks. These results suggest that improved environmental literacy and nature connectedness can help encourage citizens to act collectively to help alleviate the financial burden in dealing with environmental issues. We also found that the increased frequency of bushmeat consumption impedes people’s intention to support conservation projects, implying that the consumer culture of nature might be a hindrance in the efforts to engage the public. Based on the findings, this study offers not only the key solution to alleviate financial burdens for nature conservation but also paves the way to nurture and foster environmental culture associated with closer nature connectedness and improved consumption culture for a sustainable future in years to come.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".